In the following, we compare our SAGA with 11 related methods to illustrate its effectiveness. Among these baselines, NeighAggre [1]} is the representative one of classical profiling algorithms. VAE [2]} is a well-known auto-encoder based generative method. GCN [3]}, GraphSage [4]}, and GAT [5]} are typical graph convolutional network (GCN)-based methods, where the node representations are embedded with structure information by GCN. GraphRNA [6]} and ARWMF [7]} are representatives of attributed random walk-based methods, which apply random walks on the node-attribute bipartite graphs and can potentially tackle the attribute-missing issue. Since attribute-missing restoration is similar to cold-start recommendation problem, thus one representative cold-start recommendation method called Hers [8]} is involved as a baseline. Further, we report the performance of two attribute-incomplete methods, i.e, GINN [9]}, GCNMF [10]} and a state-of-the-art attribute-missing method, i.e., SAT [11]}. Table REF  and Table REF  summarize the performance comparison on the tasks of profiling and node classification.
